arXiv subjects

Yin Cao

Publications and source records attributed to Yin Cao.

At least 19 recordsLinked to original sources

arXiv2026

CoSTALA: Compositional Spatio-Temporal Audio-Language Alignment via Multi-Grain Hierarchical Contrastive Learning

Conventional audio language models (ALMs) have made significant progress in achieving alignment between auditory and textual representations, including recent explorations in spatial audio. However, in daily spatial scenarios, they still cannot effectively process multi-event audio sequences. Current approaches primarily rely on coarse-grained contrastive learning with global auditory and textual features, lacking the resolution to distinguish multiple sequential events. To overcome these limitations, we propose CoSTALA-a novel training paradigm that transitions from purely global alignment to fine-grained spatio-temporal reasoning. By constructing a multi-granularity hierarchical loss function system, we achieve explicit modeling of temporal dependencies, and successfully anchors individual acoustic events to preserve their semantic purity. Extensive experiments demonstrate that CoSTALA significantly establish a powerful new framework for spatio-temporal audio understanding.

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arXiv2026

Reasoning-Oriented Post-Training and Inference-Time LoRA Rescaling for Audio-Dependent Question Answering

Audio-Dependent Question Answering (ADQA) requires Large Audio-Language Models (LALMs) to answer questions whose correct answers depend on the given audio content. Successful ADQA requires accurate audio perception, identification of question-relevant evidence, and cross-modal reasoning. Using the official ADQA dataset of DCASE 2026 Task 5, we investigate reasoning-oriented post-training with Low-Rank Adaptation (LoRA) and inference-time LoRA rescaling for both Qwen2.5-Omni and MOSS-Audio-8B-Thinking. We introduce a structured Chain-of-Thought (CoT) framework that decomposes the reasoning process into question analysis, question type, audio evidence, and reasoning. We then analyze how task-specific LoRA adaptation affects the two backbones and further explore inference-time rescaling of trained LoRA adapters. Experiments on the development set reveal markedly backbone-dependent behavior: post-training improves the Qwen-based systems but substantially degrades MOSS-Audio under our supervised fine-tuning configuration. Moderate LoRA rescaling further improves the best Qwen system's top-1 accuracy from 58.93% to 61.05% and partially restores the performance of the fine-tuned MOSS-Audio models, while the best MOSS-Audio system achieves 67.70% top-1 accuracy. Our submitted systems ranked third overall and second among lightweight systems under 10B parameters in the challenge.

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arXiv2026

CogPic: A Multimodal Dataset for Early Cognitive Impairment Assessment via Picture Description Tasks

The automated evaluation of cognitive status using multimedia technologies offers a promising avenue for early dementia detection. However, the development of robust machine learning models for cognitive impairment detection is frequently hindered by the scarcity of large-scale, strictly synchronized, and clinically validated multimodal datasets. To bridge this critical gap, we introduce CogPic, a comprehensive Mandarin multimodal benchmark designed for fine-grained cognitive-status assessment. CogPic comprises strictly synchronized acoustic, visual, and linguistic data collected from 574 participants across three standardized picture-description tasks. To establish reliable diagnostic reference labels, expert clinical neuropsychologists conducted comprehensive evaluations and stratified participants into Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) groups through clinical consensus. Extensive benchmark experiments spanning handcrafted and deep learning models in unimodal and multimodal settings yield a best trimodal Macro-F1 of 58.34\%. To our knowledge, CogPic stands as the largest, most modality-rich, and most comprehensively characterized Mandarin dataset of its kind to date. Together, CogPic and its extensive baseline evaluations establish a rigorous empirical foundation for future multimedia research toward robust and clinically generalizable automated cognitive health assessment.

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arXiv2025

Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

Recent studies in speech-driven talking face generation achieve promising results, but their reliance on fixed-driven speech limits further applications (e.g., face-voice mismatch). Thus, we extend the task to a more challenging setting: given a face image and text to speak, generating both talking face animation and its corresponding speeches. Accordingly, we propose a novel framework, Face2VoiceSync, with several novel contributions: 1) Voice-Face Alignment, ensuring generated voices match facial appearance; 2) Diversity \& Manipulation, enabling generated voice control over paralinguistic features space; 3) Efficient Training, using a lightweight VAE to bridge visual and audio large-pretrained models, with significantly fewer trainable parameters than existing methods; 4) New Evaluation Metric, fairly assessing the diversity and identity consistency. Experiments show Face2VoiceSync achieves both visual and audio state-of-the-art performances on a single 40GB GPU.

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arXiv2025

SALM: Spatial Audio Language Model with Structured Embeddings for Understanding and Editing

Spatial audio understanding is essential for accurately perceiving and interpreting acoustic environments. However, existing audio-language models exhibit limitations in processing spatial audio and perceiving spatial acoustic scenes. To address this gap, we propose the Spatial Audio Language Model (SALM), a novel framework that bridges spatial audio and language through multi-modal contrastive learning. SALM integrates a text encoder with a dual-branch audio encoder that decomposes spatial sound into semantic and spatial components via structured audio embeddings. Key features of SALM include seamless alignment between spatial audio and natural language, both separate and joint extraction of spatial and semantic representations, zero-shot direction classification, and flexible support for spatial audio editing. Experimental results demonstrate that SALM effectively captures and aligns cross-modal representations, yielding well-structured audio embeddings. Furthermore, SALM enables advanced editing capabilities, such as modifying directional audio using text-based embeddings.

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arXiv2024

PSELDNets: Pre-trained Neural Networks on a Large-scale Synthetic Dataset for Sound Event Localization and Detection

Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets, have shown considerable generalization capabilities. Recently, deep neural networks trained on large-scale datasets have achieved remarkable success in the sound event classification (SEC) field, prompting an open question of whether these advances can be extended to the development of SELD foundation models. In this paper, leveraging the power of pre-trained SEC models, we propose pre-trained SELD networks (PSELDNets) on a large-scale synthetic dataset. The synthetic dataset, generated by convolving sound events with simulated spatial room impulse responses (SRIRs), contains 1,167 hours of audio clips with an ontology of 170 sound classes. These PSELDNets are applied to various SELD scenarios. When we adapt PSELDNets to specific scenarios, particularly in cases of low-resource data, we introduce a data-efficient fine-tuning method, AdapterBit. PSELDNets are evaluated on synthetic-test-set using collected SRIRs from the TAU Spatial Room Impulse Response Database (TAU-SRIR DB) and achieve satisfactory performance. We also carried out experiments to validate the transferability of PSELDNets to three publicly available datasets and our own real-world recordings. The results demonstrate that PSELDNets surpass state-of-the-art systems across all publicly available datasets. Given the need for direction-of-arrival estimation, SELD generally relies on sufficient multi-channel audio clips. However, incorporating the AdapterBit, PSELDNets show more efficient adaptability to various scenarios using minimal multi-channel or even just monophonic audio clips, outperforming traditional fine-tuning approaches.

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arXiv2024

Text-Queried Target Sound Event Localization

Sound event localization and detection (SELD) aims to determine the appearance of sound classes, together with their Direction of Arrival (DOA). However, current SELD systems can only predict the activities of specific classes, for example, 13 classes in DCASE challenges. In this paper, we propose text-queried target sound event localization (SEL), a new paradigm that allows the user to input the text to describe the sound event, and the SEL model can predict the location of the related sound event. The proposed task presents a more user-friendly way for human-computer interaction. We provide a benchmark study for the proposed task and perform experiments on datasets created by simulated room impulse response (RIR) and real RIR to validate the effectiveness of the proposed methods. We hope that our benchmark will inspire the interest and additional research for text-queried sound source localization.

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arXiv2024

Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction

Advancements in synthesized speech have created a growing threat of impersonation, making it crucial to develop deepfake algorithm recognition. One significant aspect is out-of-distribution (OOD) detection, which has gained notable attention due to its important role in deepfake algorithm recognition. However, most of the current approaches for detecting OOD in deepfake algorithm recognition rely on probability-score or classified-distance, which may lead to limitations in the accuracy of the sample at the edge of the threshold. In this study, we propose a reconstruction-based detection approach that employs an autoencoder architecture to compress and reconstruct the acoustic feature extracted from a pre-trained WavLM model. Each acoustic feature belonging to a specific vocoder class is only aptly reconstructed by its corresponding decoder. When none of the decoders can satisfactorily reconstruct a feature, it is classified as an OOD sample. To enhance the distinctiveness of the reconstructed features by each decoder, we incorporate contrastive learning and an auxiliary classifier to further constrain the reconstructed feature. Experiments demonstrate that our proposed approach surpasses baseline systems by a relative margin of 10\% in the evaluation dataset. Ablation studies further validate the effectiveness of both the contrastive constraint and the auxiliary classifier within our proposed approach.

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arXiv2024

WavCraft: Audio Editing and Generation with Large Language Models

We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing. Specifically, WavCraft describes the content of raw audio materials in natural language and prompts the LLM conditioned on audio descriptions and user requests. WavCraft leverages the in-context learning ability of the LLM to decomposes users' instructions into several tasks and tackle each task collaboratively with the particular module. Through task decomposition along with a set of task-specific models, WavCraft follows the input instruction to create or edit audio content with more details and rationales, facilitating user control. In addition, WavCraft is able to cooperate with users via dialogue interaction and even produce the audio content without explicit user commands. Experiments demonstrate that WavCraft yields a better performance than existing methods, especially when adjusting the local regions of audio clips. Moreover, WavCraft can follow complex instructions to edit and create audio content on the top of input recordings, facilitating audio producers in a broader range of applications. Our implementation and demos are available at this https://github.com/JinhuaLiang/WavCraft.

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arXiv2024

EDTC: enhance depth of text comprehension in automated audio captioning

Modality discrepancies have perpetually posed significant challenges within the realm of Automated Audio Captioning (AAC) and across all multi-modal domains. Facilitating models in comprehending text information plays a pivotal role in establishing a seamless connection between the two modalities of text and audio. While recent research has focused on closing the gap between these two modalities through contrastive learning, it is challenging to bridge the difference between both modalities using only simple contrastive loss. This paper introduces Enhance Depth of Text Comprehension (EDTC), which enhances the model's understanding of text information from three different perspectives. First, we propose a novel fusion module, FUSER, which aims to extract shared semantic information from different audio features through feature fusion. We then introduced TRANSLATOR, a novel alignment module designed to align audio features and text features along the tensor level. Finally, the weights are updated by adding momentum to the twin structure so that the model can learn information about both modalities at the same time. The resulting method achieves state-of-the-art performance on AudioCaps datasets and demonstrates results comparable to the state-of-the-art on Clotho datasets.

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arXiv2023

Selective-Memory Meta-Learning with Environment Representations for Sound Event Localization and Detection

Environment shifts and conflicts present significant challenges for learning-based sound event localization and detection (SELD) methods. SELD systems, when trained in particular acoustic settings, often show restricted generalization capabilities for diverse acoustic environments. Furthermore, obtaining annotated samples for spatial sound events is notably costly. Deploying a SELD system in a new environment requires extensive time for re-training and fine-tuning. To overcome these challenges, we propose environment-adaptive Meta-SELD, designed for efficient adaptation to new environments using minimal data. Our method specifically utilizes computationally synthesized spatial data and employs Model-Agnostic Meta-Learning (MAML) on a pre-trained, environment-independent model. The method then utilizes fast adaptation to unseen real-world environments using limited samples from the respective environments. Inspired by the Learning-to-Forget approach, we introduce the concept of selective memory as a strategy for resolving conflicts across environments. This approach involves selectively memorizing target-environment-relevant information and adapting to the new environments through the selective attenuation of model parameters. In addition, we introduce environment representations to characterize different acoustic settings, enhancing the adaptability of our attenuation approach to various environments. We evaluate our proposed method on the development set of the Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset and computationally synthesized scenes. Experimental results demonstrate the superior performance of the proposed method compared to conventional supervised learning methods, particularly in localization.

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arXiv2023

Balanced SNR-Aware Distillation for Guided Text-to-Audio Generation

Diffusion models have demonstrated promising results in text-to-audio generation tasks. However, their practical usability is hindered by slow sampling speeds, limiting their applicability in high-throughput scenarios. To address this challenge, progressive distillation methods have been effective in producing more compact and efficient models. Nevertheless, these methods encounter issues with unbalanced weights at both high and low noise levels, potentially impacting the quality of generated samples. In this paper, we propose the adaptation of the progressive distillation method to text-to-audio generation tasks and introduce the Balanced SNR-Aware~(BSA) method, an enhanced loss-weighting mechanism for diffusion distillation. The BSA method employs a balanced approach to weight the loss for both high and low noise levels. We evaluate our proposed method on the AudioCaps dataset and report experimental results showing superior performance during the reverse diffusion process compared to previous distillation methods with the same number of sampling steps. Furthermore, the BSA method allows for a significant reduction in sampling steps from 200 to 25, with minimal performance degradation when compared to the original teacher models.

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arXiv2023

WavJourney: Compositional Audio Creation with Large Language Models

Despite breakthroughs in audio generation models, their capabilities are often confined to domain-specific conditions such as speech transcriptions and audio captions. However, real-world audio creation aims to generate harmonious audio containing various elements such as speech, music, and sound effects with controllable conditions, which is challenging to address using existing audio generation systems. We present WavJourney, a novel framework that leverages Large Language Models (LLMs) to connect various audio models for audio creation. WavJourney allows users to create storytelling audio content with diverse audio elements simply from textual descriptions. Specifically, given a text instruction, WavJourney first prompts LLMs to generate an audio script that serves as a structured semantic representation of audio elements. The audio script is then converted into a computer program, where each line of the program calls a task-specific audio generation model or computational operation function. The computer program is then executed to obtain a compositional and interpretable solution for audio creation. Experimental results suggest that WavJourney is capable of synthesizing realistic audio aligned with textually-described semantic, spatial and temporal conditions, achieving state-of-the-art results on text-to-audio generation benchmarks. Additionally, we introduce a new multi-genre story benchmark. Subjective evaluations demonstrate the potential of WavJourney in crafting engaging storytelling audio content from text. We further demonstrate that WavJourney can facilitate human-machine co-creation in multi-round dialogues. To foster future research, the code and synthesized audio are available at: https://audio-agi.github.io/WavJourney_demopage/.

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arXiv2023

META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection

For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences in the validation and evaluation stages. Different environments, such as different sizes of rooms, different reverberation times, and different background noise, may be reasons for a learning-based system to fail. On the other hand, acquiring annotated spatial sound event samples, which include onset and offset time stamps, class types of sound events, and direction-of-arrival (DOA) of sound sources is very expensive. In addition, deploying a SELD system in a new environment often poses challenges due to time-consuming training and fine-tuning processes. To address these issues, we propose Meta-SELD, which applies meta-learning methods to achieve fast adaptation to new environments. More specifically, based on Model Agnostic Meta-Learning (MAML), the proposed Meta-SELD aims to find good meta-initialized parameters to adapt to new environments with only a small number of samples and parameter updating iterations. We can then quickly adapt the meta-trained SELD model to unseen environments. Our experiments compare fine-tuning methods from pre-trained SELD models with our Meta-SELD on the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSSS23) dataset. The evaluation results demonstrate the effectiveness of Meta-SELD when adapting to new environments.

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arXiv2022

Neural Sound Field Decomposition with Super-resolution of Sound Direction

Sound field decomposition predicts waveforms in arbitrary directions using signals from a limited number of microphones as inputs. Sound field decomposition is fundamental to downstream tasks, including source localization, source separation, and spatial audio reproduction. Conventional sound field decomposition methods such as Ambisonics have limited spatial decomposition resolution. This paper proposes a learning-based Neural Sound field Decomposition (NeSD) framework to allow sound field decomposition with fine spatial direction resolution, using recordings from microphone capsules of a few microphones at arbitrary positions. The inputs of a NeSD system include microphone signals, microphone positions, and queried directions. The outputs of a NeSD include the waveform and the presence probability of a queried position. We model the NeSD systems respectively with different neural networks, including fully connected, time delay, and recurrent neural networks. We show that the NeSD systems outperform conventional Ambisonics and DOANet methods in sound field decomposition and source localization on speech, music, and sound events datasets. Demos are available at https://www.youtube.com/watch?v=0GIr6doj3BQ.

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arXiv2022

Sound Event Localization and Detection for Real Spatial Sound Scenes: Event-Independent Network and Data Augmentation Chains

Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from computationally generated spatial recordings to recordings of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on our previous proposed Event-Independent Network V2 (EINV2) with a novel data augmentation method. Our method employs EINV2 with a track-wise output format, permutation-invariant training, and a soft parameter-sharing strategy, to detect different sound events of the same class but in different locations. The Conformer structure is used for extending EINV2 to learn local and global features. A data augmentation method, which contains several data augmentation chains composed of stochastic combinations of several different data augmentation operations, is utilized to generalize the model. To mitigate the lack of real-scene recordings in the development dataset and the presence of sound events being unbalanced, we exploit FSD50K, AudioSet, and TAU Spatial Room Impulse Response Database (TAU-SRIR DB) to generate simulated datasets for training. We present results on the validation set of Sony-TAu Realistic Spatial Soundscapes 2022 (STARSS22) in detail. Experimental results indicate that the ability to generalize to different environments and unbalanced performance among different classes are two main challenges. We evaluate our proposed method in Task 3 of the DCASE 2022 challenge and obtain the second rank in the teams ranking. Source code is released.

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arXiv2022

A Track-Wise Ensemble Event Independent Network for Polyphonic Sound Event Localization and Detection

Polyphonic sound event localization and detection (SELD) aims at detecting types of sound events with corresponding temporal activities and spatial locations. In this paper, a track-wise ensemble event independent network with a novel data augmentation method is proposed. The proposed model is based on our previous proposed Event-Independent Network V2 and is extended by conformer blocks and dense blocks. The track-wise ensemble model with track-wise output format is proposed to solve an ensemble model problem for track-wise output format that track permutation may occur among different models. The data augmentation approach contains several data augmentation chains, which are composed of random combinations of several data augmentation operations. The method also utilizes log-mel spectrograms, intensity vectors, and Spatial Cues-Augmented Log-Spectrogram (SALSA) for different models. We evaluate our proposed method in the Task of the L3DAS22 challenge and obtain the top ranking solution with a location-dependent F-score to be 0.699. Source code is released.

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arXiv2022

ARCA23K: An audio dataset for investigating open-set label noise

The availability of audio data on sound sharing platforms such as Freesound gives users access to large amounts of annotated audio. Utilising such data for training is becoming increasingly popular, but the problem of label noise that is often prevalent in such datasets requires further investigation. This paper introduces ARCA23K, an Automatically Retrieved and Curated Audio dataset comprised of over 23000 labelled Freesound clips. Unlike past datasets such as FSDKaggle2018 and FSDnoisy18K, ARCA23K facilitates the study of label noise in a more controlled manner. We describe the entire process of creating the dataset such that it is fully reproducible, meaning researchers can extend our work with little effort. We show that the majority of labelling errors in ARCA23K are due to out-of-vocabulary audio clips, and we refer to this type of label noise as open-set label noise. Experiments are carried out in which we study the impact of label noise in terms of classification performance and representation learning.

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